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Assessing the Suitability of Artificial Intelligence to Accomplish Organizational Finance Tasks

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Information Integration and Web Intelligence (iiWAS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15343))

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Abstract

Artificial Intelligence (AI) holds transformative potential for many fields. However, identifying suitable tasks for artificial intelligence implementation remains a challenge. This study proposes an Artificial Intelligence Readiness Task Assessment (AIRTA) tool, empowering finance professionals to assess task suitability for AI implementation. Artificial intelligence adoption often encounters costs, compatibility, and skill gaps. The proposed AIRTA addresses these challenges. AIRTA is designed following the design science research approach, ensuring it is user-friendly and effectively addresses real-world challenges. AIRTA consists of three sections: task framing, task assessment, and results interpretation. Unlike existing methodologies focusing on organization-wide artificial intelligence readiness, the proposed tool centers on task-specific readiness. This innovative approach provides practical guidance for finance professionals seeking to leverage artificial intelligence and helps organizations realize the potential of AI more effectively.

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References

  1. Jöhnk, J., Weißert, M., Wyrtki, K.: Ready or not, AI comes—an interview study of organizational AI readiness factors. Bus. Inf. Syst. Eng. 63, 5–20 (2021)

    Article  Google Scholar 

  2. Radhakrishnan, J., Chattopadhyay, M.: Determinants and barriers of artificial intelligence adoption – a literature review. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Rana, N.P. (eds) Re-imagining diffusion and adoption of information technology and systems: a continuing conversation. TDIT 2020. IFIP Advances in Information and Communication Technology, vol. 617. Springer, Cham (2020)

    Google Scholar 

  3. Enholm, I.M., Papagiannidis, E., Mikalef, P., et al.: Artificial intelligence and business value: a literature review. Inf. Syst. Front. 24, 1709–1734 (2022)

    Article  Google Scholar 

  4. Ransbotham, S., Kiron, D., Gerbert, P., Reeves, M.: Reshaping business with artificial intelligence: closing the gap between ambition and action. MIT Sloan Manag. Rev. 59(1) (2017)

    Google Scholar 

  5. AI Readiness Index (AIRI). AI Singapore. http://aisingapore.org/innovation/airi. Accessed 12 Apr 2023

  6. Holmström, J.: From AI to digital transformation: the AI readiness framework. Bus. Horiz. 65(3), 329–339 (2022)

    Article  Google Scholar 

  7. Alsheibani, S., Cheung, Y., Messom, C.: Artificial intelligence adoption: AI-readiness at firm-level. PACIS 4, 231–245 (2018)

    Google Scholar 

  8. Alsheibani, S., Cheung, Y., Messom, C.: Towards an artificial intelligence maturity model: from science fiction to business facts. In: PACIS, p. 46 (2019)

    Google Scholar 

  9. Wang, X., Li, L., Tan, S.C., Yang, L., Lei, J.: Preparing for AI-enhanced education: Conceptualizing and empirically examining teachers’ AI readiness. Comput. Hum. Behav. 146, 107798 (2023)

    Google Scholar 

  10. Chowdhury, S., et al.: Unlocking the value of artificial intelligence in human resource management through AI capability framework. HR Manag. Rev. 33(1), 100899 (2023)

    Google Scholar 

  11. Cao, L.: AI in finance: challenges, techniques, and opportunities. ACM Comput. Surv. 55(3), 1–38 (2022)

    Article  Google Scholar 

  12. Rimol, M.: Gartner survey reveals 80% of executives think automation can be applied to any business decision. Gartner. http://www.gartner.com/en/newsroom/press-releases/2022-08-22-gartner-survey-reveals-80-percent-of-executives-think-automation-can-be-applied-to-any-business-decision. Accessed 22 Aug 2022

  13. Gama, F., Tyskbo, D., Nygren, J., Barlow, J., Reed, J., Svedberg, P.: Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J. Med. Internet Res. 24(1), e32215 (2022). https://doi.org/10.2196/32215

  14. Treveil, M., et al.: Introducing Mlops How to Scale Machine Learning in the Enterprise. O’Reilly (2021)

    Google Scholar 

  15. Brealey, R.A., Myers, S.C., Marcus, A.: Fundamentals of Corporate Finance. McGraw Hill (2023)

    Google Scholar 

  16. Sadiq, R.B., Safie, N., Abd Rahman, A.H., Goudarzi, S.: Artificial intelligence maturity model: a systematic literature review. PeerJ Comput. Sci. 7, e661 (2021). https://doi.org/10.7717/peerj-cs.661

    Article  Google Scholar 

  17. Davenport, T.H., Ronanki, R.: Artificial Intelligence for the Real World. Harvard Business Review (2018)

    Google Scholar 

  18. Intelligent Artifacts: AI Readiness Assessment. Intelligent Artifacts | AI Readiness Assessment. http://www.intelligent-artifacts.com/aira. Accessed 21 Apr 2023

  19. UNESCO. A Tool of the Recommendation on the Ethics of Artificial Intelligence: Readiness Assessment Methodology. http://unesco.org. Accessed 10 May 2023

  20. Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A design science research methodology for information systems research. J. MIS 24(3), 45–77 (2007)

    Google Scholar 

  21. Alwosheel, A., van Cranenburgh, S., Chorus, C.G.: Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. J. Choice Model. 28, 167–182 (2018)

    Article  Google Scholar 

  22. Audit Screen. https://auditscreen.org/#:~:text=Alcohol%20Use%20Disorders%20Identification%20Test,or%20any%20alcohol%20use%20disorder. Accessed 12 May 2023

    Google Scholar 

  23. DiCicco-Bloom, B., Crabtree, B.F.: The qualitative research interview. Med. Educ. 40(4), 314–321 (2006)

    Article  Google Scholar 

  24. von Garrel, J., Jahn, C.: Design framework for the implementation of AI-based (service) business models for small and medium-sized manufacturing enterprises. J. Knowl. Econ. (2022)

    Google Scholar 

  25. Shaw, J., Rudzicz, F., Jamieson, T., Goldfarb, A.: Artificial intelligence and the implementation challenge. J. Med. Internet Res. 21(7), e13659 (2019)

    Google Scholar 

  26. Luger, E., Sellen, A.: “Like having a really bad PA”: the gulf between user expectation and experience of conversational agents. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 5286–5297 (2016)

    Google Scholar 

  27. Tversky, A., Kahneman, D.: The framing of decisions and the psychology of choice. Science 211(4481), 453–458 (1981)

    Article  MathSciNet  Google Scholar 

  28. Makridakis, S.: The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures 90, 46–60 (2017)

    Article  Google Scholar 

  29. Alpaydin, E.: Introduction to Machine Learning. The MIT Press (2010)

    Google Scholar 

  30. Shu-Hsien, L.: Expert system methodologies and applications—a decade review from 1995 to 2004. Expert Syst. Appl. 28(1), 93–103 (2005)

    Article  Google Scholar 

  31. Nadkarni, P.M., Ohno-Machado, L., Chapman, W.W.: Natural language processing: an introduction. J. Am. Med. Inform. Assoc. 18(5), 544–551 (2011)

    Article  Google Scholar 

  32. Chakraborti, T., et al.: From robotic process automation to intelligent process automation. In: Lecture Notes in Business Information Processing, pp. 215–228. Springer, Cham (2020)

    Google Scholar 

  33. Petersson, L., et al.: Challenges to implementing artificial intelligence in Healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Serv. Res. 22(1) (2022)

    Google Scholar 

  34. Nortje, M.A., Grobbelaar, S.S.: A framework for the implementation of Artificial Intelligence in business enterprises: a readiness model. In: 2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (2020)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer and Systems Science, Stockholm University, Stockholm, Sweden

    Gabriel Smith & Workneh Yilma Ayele

Authors
  1. Gabriel Smith
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  2. Workneh Yilma Ayele
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Correspondence to Gabriel Smith .

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Editors and Affiliations

  1. Monash University, Clayton, VIC, Australia

    Pari Delir Haghighi

  2. Comenius University in Bratislava, Bratislava, Slovakia

    Michal Greguš

  3. Johannes Kepler University, Linz, Austria

    Gabriele Kotsis

  4. Johannes Kepler University, Linz, Austria

    Ismail Khalil

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Smith, G., Ayele, W.Y. (2025). Assessing the Suitability of Artificial Intelligence to Accomplish Organizational Finance Tasks. In: Delir Haghighi, P., Greguš, M., Kotsis, G., Khalil, I. (eds) Information Integration and Web Intelligence. iiWAS 2024. Lecture Notes in Computer Science, vol 15343. Springer, Cham. https://doi.org/10.1007/978-3-031-78093-6_24

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  • DOI: https://doi.org/10.1007/978-3-031-78093-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-78092-9

  • Online ISBN: 978-3-031-78093-6

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